Research on Moving Object Detection and Tracking in Video Images
|Course||Resources and Environmental Remote Sensing|
|Keywords||Video images Moving objects detection Background modeling Moving objectstracking Mean shift Kalman filtering Particle filtering Fourier-Mellin transform|
This thesis is concerned with moving single target detection and visual trackingalgorithms and their application in the real world. For a long time, it is the ultimate goal toestablish ideal background image by different mathematical mode tools in moving objectdetection field. According to the actual complex background environment (Lighting change,subtle moving, etc.) in video images, an adaptive background modeling method is proposedfor moving object detection based on Kalman filter theories, this method can be adapted tocomplex background environment, extracting background image close to real backgroundscene. At the same time, three different methods of moving object tracking is presented, thoseis an adaptive Mean Shift tracking algorithm, a tracking algorithm fusion of color andtexture features under the framework of particle filter and the algorithm of moving objecttracking based on Fourier-Mellin Transform. The experiments result is that each method isrobust and adapted to the different conditions.With the help of the Gaussian model theory, kalman filtering ideas, mean shift algorithm,Bayesian theory, wavelet function theory, particle filtering method and Fourier-MellinTransform(FMT) method, this paper carry out a lot of research work in moving objectdetection and tracking of the video images or image sequence.In this paper the main researchwork and innovative ideas are as follows:(1) In part of detecting and retrieving the foreground, this paper analysis several genericbackground modeling methods， especially for the mixture Gaussian model in detail, analgorithm of moving object detection is proposed based on kalman filtering backgroundmodeling. The method estimates the background of video images or image sequence by theprevious frame image and the current observation image, and it is iterative, stable andunbiased. The presented method overcomes shortcomings of the traditional averagebackground model (ABM) method that it is sensitive to light changes and multi-mode distribution of background pixel. The amount of calculation is less MoG method based onstatistical theory.(2) Researching on the theory of Mean Shift (MS), an adaptive tracking algorithm is proposedbased on mean shift. In the traditional MS tracking method, the correlation between templateimage and observed image is measured by Bhattacharyya coefficient. In this paper, the objectcentroid is over and over again calculated by0-order moment and tow1-order moment whichgot from two dimensional gray histogram of target images area. The position of the trackedobjects is adjusted based on the object centroid so that it moves to the real target center. Thismethod overcomes the deficiency of the traditional MS tracking algorithm when the overlapregion of tracked object in the adjacent frames is too small or no overlap area to track thetarget.(3) Researching on objects tracking algorithm based on particle filter, the tracking algorithmis proposed to fusion of color and texture features under the framework of particle filter. Thepresented method requires less number of particles can achieve the desired trackingperformance compared with the single feature tracking algorithm in particle filter, and itimproves the particle degeneracy in certain extent.(4) Researching on the images matching algorithms and the characteristics of visible lightimages, the algorithm of moving object tracking is presented based on Fourier-MellinTransform. The method is based on the idea of image matching, it can estimate the size andposition of the tracking window by matching the tracking object with the observed image, andupdate the tracking target template by the translation, rotation and zoom parameters based onimages matching, the proposed algorithm achieved tracking moving object which size andposture changed in moving.